Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
A spatial data mining method by Delaunay triangulation
GIS '97 Proceedings of the 5th ACM international workshop on Advances in geographic information systems
Detecting graph-based spatial outliers: algorithms and applications (a summary of results)
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
OPTICS-OF: Identifying Local Outliers
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Detecting Spatial Outliers with Multiple Attributes
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Neighborhood based detection of anomalies in high dimensional spatio-temporal sensor datasets
Proceedings of the 2004 ACM symposium on Applied computing
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
FS3: A Random Walk Based Free-Form Spatial Scan Statistic for Anomalous Window Detection
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Inter-image outliers and their application to image classification
Pattern Recognition
Hi-index | 0.00 |
Spatial outlier detection approaches identify outliers by first defining a spatial neighborhood. However, existing approaches suffer from two issues: (1) they primarily consider autocorrelation alone in forming the neighborhood, but ignore heterogeneity among spatial objects. (2) they do not consider interrelationships among the attributes for identifying how distinct the object is with respect to its neighbors, but consider them independently (either single or multiple). As a result, one may not identify truly unusual spatial objects and may also end up with frivolous outliers. In this paper, we revisit the computation of the spatial neighborhood and propose an approach to address the above two issues. We begin our approach with identifying a spatially related neighborhood, capturing autocorrelation. We then consider interrelationships between attributes and multiple, multilevel distributions within these attributes, thus considering autocorrelation and heterogeneity in various forms. Subsequently, we identify outliers in these neighborhoods. Our experimental results in various datasets (North Carolina SIDS data, New Mexico Leukemia data, etc.) indicate that our approach indeed correctly identifies outliers in heterogeneous neighborhoods.